Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [17]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
from collections import deque
%matplotlib inline
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
#foldername = "C:\Users\loynin\Documents\Self-Driving Car2\camera_cal"
cal_image_fnames = glob.glob(r'camera_cal\calibration*.jpg')
test_image_fnames = glob.glob(r'test_images\test*.jpg')
single_test_image_fname = (r'test_images\test2.jpg')
test_image_binary = cv2.imread(single_test_image_fname)
#This function is used to show two images.
def to_compare_image(first_image, second_image, dest_title = "Processed Image",gray=False):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
f.tight_layout()
ax1.imshow(cv2.cvtColor(first_image, cv2.COLOR_BGR2RGB).astype('uint8'))
ax1.set_title("Original Image", fontsize=20)
if gray:
ax2.imshow(second_image,cmap='gray')
else:
ax2.imshow(cv2.cvtColor(second_image, cv2.COLOR_BGR2RGB).astype('uint8'))
ax2.set_title(dest_title, fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
#function uses to undistort the input image
def to_undistort_image(img, objpoints = objpoints, imgpoints = imgpoints):
# Use cv2.calibrateCamera() and cv2.undistort()
#undist = np.copy(img) # Delete this line
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img.shape[0:2], None, None)
undist = cv2.undistort(img,mtx,dist,None,mtx)
return undist
# Step through the list and search for chessboard corners
for fname in cal_image_fnames:
img = cv2.imread(fname)
ori_image = np.copy(img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img_corners = cv2.drawChessboardCorners(img, (9,6), corners, ret)
to_compare_image(ori_image,img_corners,"Draw Corners")
print('done')
Correct distortion and warp image¶
In [18]:
# Undistort test images and display the original images and undistorted images.
for fname in test_image_fnames:
image = cv2.imread(fname)
print (image.shape)
undistorted = to_undistort_image(image)
to_compare_image(image,undistorted,"Undistorted Image")
In [19]:
#Warp the images to find the lane line
def to_warp_image(img, display = True):
undistorted = to_undistort_image(img)
image_size = (undistorted.shape[1], undistorted.shape[0])
offset = 0
# Set the original region of interest and destination of region of interest
src = np.float32([[490, 482],[810, 482],
[1250, 720],[40, 720]])
dst = np.float32([[0, 0], [1280, 0],
[1250, 720],[40, 720]])
M_warp = cv2.getPerspectiveTransform(src, dst)
M_unwarp = cv2.getPerspectiveTransform(dst, src)
warped_image = cv2.warpPerspective(undistorted, M_warp, image_size)
if display:
to_compare_image(undistorted,warped_image,"Undistorted & Warped")
else:
return warped_image, M_warp , M_unwarp
print ('function load...')
In [20]:
for fname in test_image_fnames:
image = cv2.imread(fname)
to_warp_image(image)
print('done')
In [21]:
# threshold the image by applying s channel
def s_select(img):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
s_thresh_min = 180
s_thresh_max = 255
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel >s_thresh_min) & (s_channel <= s_thresh_max)] = 1
return binary_output
# threshold the image by applying l channel
def l_select(img):
l_channel = cv2.cvtColor(img, cv2.COLOR_BGR2LUV)[:,:,0]
l_thresh_min = 225
l_thresh_max = 255
l_binary = np.zeros_like(l_channel)
l_binary[(l_channel >= l_thresh_min) & (l_channel <= l_thresh_max)] = 1
return l_binary
# threshold the image by applying b channel
def b_select(img):
b_channel = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:,:,2]
b_thresh_min = 155
b_thresh_max = 255
b_binary = np.zeros_like(b_channel)
b_binary[(b_channel >= b_thresh_min) & (b_channel <= b_thresh_max)] = 1
return b_binary
print('Done')
In [22]:
# Combine threshold to find the best lane line
def to_combine_thresholds(img):
img = np.copy(img)
s_binary = s_select(img)
l_binary = l_select(img)
b_binary = b_select(img)
combined_binary = np.zeros_like(s_binary)
combined_binary[(l_binary == 1) | (b_binary == 1)] = 1
return combined_binary
warped_image_sample , M_warp , M_unwarp = to_warp_image(test_image_binary, display = False)
result = to_combine_thresholds(warped_image_sample)
#hls_binary = hls_select(result, thresh=(90, 255))
to_compare_image(image,result,"Threshold Image",True)
In [23]:
####### Apply each of the thresholding functions
for fname in test_image_fnames:
img = cv2.imread(fname)
image , M , Minv = to_warp_image(img, display = False)
combined_img = to_combine_thresholds(image)
to_compare_image(image,combined_img,"Combined Thresholds",True)
print('Done')
In [24]:
# Warp the image, find the lane line and then draw the line on to the warped image.
def to_slide_window(original_image, warped_image, display = False):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
#print('warped image shape', result.shape[0])
binary_warped = warped_image
new_img = np.copy(original_image)
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 25
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
#######################################
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 50
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
leftx_int = left_fit[0]*720**2 + left_fit[1]*720 + left_fit[2]
rightx_int = right_fit[0]*720**2 + right_fit[1]*720 + right_fit[2]
#And you're done! But let's visualize the result here as well
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
warp_zero = np.zeros_like(warped_image).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
h = warped_image.shape[0]
w = warped_image.shape[1]
ploty = np.linspace(0, h-1, num=h)# to cover same y-range as image
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
cv2.polylines(color_warp, np.int32([pts_left]), isClosed=False, color=(255,0,255), thickness=15)
cv2.polylines(color_warp, np.int32([pts_right]), isClosed=False, color=(0,255,255), thickness=15)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (w, h))
# Combine the result with the original image
result_mark = cv2.addWeighted(new_img, 1, newwarp, 0.5, 0)
#################################################################################################
#Measure radius of Curvature of each land line
ym_per_pix = 30./720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meteres per pixel in x dimension
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
center = (left_curverad + right_curverad)/2
print ('left_curv, right_crve, radius',left_curverad,right_curverad, center )
print ('lefy,leftx,rifhty,rightx', lefty,leftx,righty,rightx)
##### Vehicle Position
camera_position = binary_warped.shape[1]/2
lane_center= (left_fitx[binary_warped.shape[0]-1] + right_fitx[binary_warped.shape[0]-1])/2
center_offset_meter = abs(camera_position - lane_center) * xm_per_pix
if center <0:
cv2.putText(result_mark,'Vehicle is {:.2f}m left of center'.format(-center_offset_meter),(200,100), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
else:
cv2.putText(result_mark,'Vehicle is {:.2f}m right of center'.format(center_offset_meter),(200,100), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
cv2.putText(result_mark,'Radius of curvature is {}m'.format(int(center)),(200,175), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
if display:
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(binary_warped)
plt.show()
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.show()
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.8, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.show()
# Draw the lane onto the warped blank image
plt.imshow(result_mark)
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.show()
else:
return left_fit, right_fit, left_curverad, right_curverad , center_offset_meter
to_slide_window(test_image_binary, result, True)
In [25]:
# Read in a thresholded image
warped = result#mpimg.imread('warped_example.jpg')
def to_draw_lane(original_img, binary_img, l_fit, r_fit, Minv,left_curverad,right_curverad,center_offset_meter ):
new_img = np.copy(original_img)
if l_fit is None or r_fit is None:
return original_img
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
h = binary_img.shape[0]
w = binary_img.shape[1]
ploty = np.linspace(0, h-1, num=h)# to cover same y-range as image
left_fitx = l_fit[0]*ploty**2 + l_fit[1]*ploty + l_fit[2]
right_fitx = r_fit[0]*ploty**2 + r_fit[1]*ploty + r_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
cv2.polylines(color_warp, np.int32([pts_left]), isClosed=False, color=(255,0,255), thickness=15)
cv2.polylines(color_warp, np.int32([pts_right]), isClosed=False, color=(0,255,255), thickness=15)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (w, h))
# Combine the result with the original image
result = cv2.addWeighted(new_img, 1, newwarp, 0.5, 0)
radius_curverad = (left_curverad + right_curverad)/2
if center_offset_meter <0:
cv2.putText(result,'Vehicle is {:.2f}m left of center'.format(-center_offset_meter),(200,100), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
else:
cv2.putText(result,'Vehicle is {:.2f}m right of center'.format(center_offset_meter),(200,100), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
cv2.putText(result,'Radius of curvature is {}m'.format(int(radius_curverad)),(200,175), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
return result
print('done')
In [26]:
for fname in test_image_fnames:
img = cv2.imread(fname)
image , M , Minv = to_warp_image(img, display = False)
#image, M = birdeye_view(img,display= False)
combined_img = to_combine_thresholds(image)
left_fit, right_fit , left_c, right_c, center = to_slide_window(img,combined_img)
out_image = to_draw_lane(img, combined_img, left_fit, right_fit, Minv,left_c, right_c,center)
to_compare_image(image,out_image,"Combined Thresholds",True)
In [27]:
class Line:
def __init__(self):
# Was the line found in the previous frame?
self.found = False
# Remember x and y values of lanes in previous frame
self.X = None
self.Y = None
# Store recent x intercepts for averaging across frames
self.x_int = deque(maxlen=10)
self.top = deque(maxlen=10)
# Remember previous x intercept to compare against current one
self.lastx_int = None
self.last_top = None
# Remember radius of curvature
self.radius = None
# Store recent polynomial coefficients for averaging across frames
self.fit0 = deque(maxlen=10)
self.fit1 = deque(maxlen=10)
self.fit2 = deque(maxlen=10)
self.fitx = None
self.pts = []
# Count the number of frames
self.count = 0
def found_search(self, x, y):
'''
This function is applied when the lane lines have been detected in the previous frame.
It uses a sliding window to search for lane pixels in close proximity (+/- 25 pixels in the x direction)
around the previous detected polynomial.
'''
xvals = []
yvals = []
if self.found == True:
i = 720
j = 630
while j >= 0:
yval = np.mean([i,j])
xval = (np.mean(self.fit0))*yval**2 + (np.mean(self.fit1))*yval + (np.mean(self.fit2))
x_idx = np.where((((xval - 25) < x)&(x < (xval + 25))&((y > j) & (y < i))))
x_window, y_window = x[x_idx], y[x_idx]
if np.sum(x_window) != 0:
np.append(xvals, x_window)
np.append(yvals, y_window)
i -= 90
j -= 90
if np.sum(xvals) == 0:
self.found = False # If no lane pixels were detected then perform blind search
return xvals, yvals, self.found
def blind_search(self, x, y, image):
'''
This function is applied in the first few frames and/or if the lane was not successfully detected
in the previous frame. It uses a slinding window approach to detect peaks in a histogram of the
binary thresholded image. Pixels in close proimity to the detected peaks are considered to belong
to the lane lines.
'''
xvals = []
yvals = []
if self.found == False:
i = 720
j = 630
while j >= 0:
histogram = np.sum(image[j:i,:], axis=0)
if self == Right:
peak = np.argmax(histogram[640:]) + 640
else:
peak = np.argmax(histogram[:640])
x_idx = np.where((((peak - 25) < x)&(x < (peak + 25))&((y > j) & (y < i))))
x_window, y_window = x[x_idx], y[x_idx]
if np.sum(x_window) != 0:
xvals.extend(x_window)
yvals.extend(y_window)
i -= 90
j -= 90
if np.sum(xvals) > 0:
self.found = True
else:
yvals = self.Y
xvals = self.X
return xvals, yvals, self.found
def radius_of_curvature(self, xvals, yvals):
ym_per_pix = 30./720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meteres per pixel in x dimension
fit_cr = np.polyfit(yvals*ym_per_pix, xvals*xm_per_pix, 2)
curverad = ((1 + (2*fit_cr[0]*np.max(yvals) + fit_cr[1])**2)**1.5) \
/np.absolute(2*fit_cr[0])
return curverad
def sort_vals(self, xvals, yvals):
sorted_index = np.argsort(yvals)
sorted_yvals = yvals[sorted_index]
sorted_xvals = xvals[sorted_index]
return sorted_xvals, sorted_yvals
def get_intercepts(self, polynomial):
bottom = polynomial[0]*720**2 + polynomial[1]*720 + polynomial[2]
top = polynomial[0]*0**2 + polynomial[1]*0 + polynomial[2]
return bottom, top
In [28]:
def image_processing(image):
img_size = (image.shape[1], image.shape[0])
# Calibrate camera and undistort image
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
undist = cv2.undistort(image, mtx, dist, None, mtx)
# Perform perspective transform
offset = 0
src = np.float32([[490, 482],[810, 482],
[1250, 720],[0, 720]])
dst = np.float32([[0, 0], [1280, 0],
[1250, 720],[40, 720]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(undist, M, img_size)
binary_warped = warped
# Generate binary thresholded images
b_channel = cv2.cvtColor(warped, cv2.COLOR_RGB2Lab)[:,:,2]
l_channel = cv2.cvtColor(warped, cv2.COLOR_RGB2LUV)[:,:,0]
# Set the upper and lower thresholds for the b channel
b_thresh_min = 145
b_thresh_max = 200
b_binary = np.zeros_like(b_channel)
b_binary[(b_channel >= b_thresh_min) & (b_channel <= b_thresh_max)] = 1
# Set the upper and lower thresholds for the l channel
l_thresh_min = 215
l_thresh_max = 255
l_binary = np.zeros_like(l_channel)
l_binary[(l_channel >= l_thresh_min) & (l_channel <= l_thresh_max)] = 1
combined_binary = np.zeros_like(b_binary)
combined_binary[(l_binary == 1) | (b_binary == 1)] = 1
# Identify all non zero pixels in the image
x, y = np.nonzero(np.transpose(combined_binary))
if Left.found == True: # Search for left lane pixels around previous polynomial
leftx, lefty, Left.found = Left.found_search(x, y)
if Right.found == True: # Search for right lane pixels around previous polynomial
rightx, righty, Right.found = Right.found_search(x, y)
if Right.found == False: # Perform blind search for right lane lines
rightx, righty, Right.found = Right.blind_search(x, y, combined_binary)
if Left.found == False:# Perform blind search for left lane lines
leftx, lefty, Left.found = Left.blind_search(x, y, combined_binary)
lefty = np.array(lefty).astype(np.float32)
leftx = np.array(leftx).astype(np.float32)
righty = np.array(righty).astype(np.float32)
rightx = np.array(rightx).astype(np.float32)
# Calculate left polynomial fit based on detected pixels
left_fit = np.polyfit(lefty, leftx, 2)
# Calculate intercepts to extend the polynomial to the top and bottom of warped image
leftx_int, left_top = Left.get_intercepts(left_fit)
# Average intercepts across n frames
Left.x_int.append(leftx_int)
Left.top.append(left_top)
leftx_int = np.mean(Left.x_int)
left_top = np.mean(Left.top)
Left.lastx_int = leftx_int
Left.last_top = left_top
# Add averaged intercepts to current x and y vals
leftx = np.append(leftx, leftx_int)
lefty = np.append(lefty, 720)
leftx = np.append(leftx, left_top)
lefty = np.append(lefty, 0)
# Sort detected pixels based on the yvals
leftx, lefty = Left.sort_vals(leftx, lefty)
Left.X = leftx
Left.Y = lefty
# Recalculate polynomial with intercepts and average across n frames
left_fit = np.polyfit(lefty, leftx, 2)
Left.fit0.append(left_fit[0])
Left.fit1.append(left_fit[1])
Left.fit2.append(left_fit[2])
left_fit = [np.mean(Left.fit0),
np.mean(Left.fit1),
np.mean(Left.fit2)]
# Fit polynomial to detected pixels
left_fitx = left_fit[0]*lefty**2 + left_fit[1]*lefty + left_fit[2]
Left.fitx = left_fitx
# Calculate right polynomial fit based on detected pixels
right_fit = np.polyfit(righty, rightx, 2)
# Calculate intercepts to extend the polynomial to the top and bottom of warped image
rightx_int, right_top = Right.get_intercepts(right_fit)
# Average intercepts across 5 frames
Right.x_int.append(rightx_int)
rightx_int = np.mean(Right.x_int)
Right.top.append(right_top)
right_top = np.mean(Right.top)
Right.lastx_int = rightx_int
Right.last_top = right_top
rightx = np.append(rightx, rightx_int)
righty = np.append(righty, 720)
rightx = np.append(rightx, right_top)
righty = np.append(righty, 0)
# Sort right lane pixels
rightx, righty = Right.sort_vals(rightx, righty)
Right.X = rightx
Right.Y = righty
# Recalculate polynomial with intercepts and average across n frames
right_fit = np.polyfit(righty, rightx, 2)
Right.fit0.append(right_fit[0])
Right.fit1.append(right_fit[1])
Right.fit2.append(right_fit[2])
right_fit = [np.mean(Right.fit0), np.mean(Right.fit1), np.mean(Right.fit2)]
# Fit polynomial to detected pixels
right_fitx = right_fit[0]*righty**2 + right_fit[1]*righty + right_fit[2]
Right.fitx = right_fitx
Minv = cv2.getPerspectiveTransform(dst, src)
#print('lefty,leftx,righty,rightx', lefty,leftx,righty,rightx)
#################################################################################################
#Measure radius of Curvature of each land line
ym_per_pix = 30./720 # meters per pixel in y dimensio
xm_per_pix = 3.7/700 # meteres per pixel in x dimension
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
center = (left_curverad + right_curverad)/2
#print ('left_curv, right_crve, radius',left_curverad,right_curverad, center )
##### Vehicle Position
camera_position = binary_warped.shape[1]/2
lane_center= (left_fitx[binary_warped.shape[0]-1] + right_fitx[binary_warped.shape[0]-1])/2
center_offset_meter = abs(camera_position - lane_center) * xm_per_pix
warp_zero = np.zeros_like(combined_binary).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.flipud(np.transpose(np.vstack([Left.fitx, Left.Y])))])
pts_right = np.array([np.transpose(np.vstack([right_fitx, Right.Y]))])
pts = np.hstack((pts_left, pts_right))
cv2.polylines(color_warp, np.int_([pts]), isClosed=False, color=(0,0,255), thickness = 40)
cv2.fillPoly(color_warp, np.int_(pts), (34,255,34))
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
result = cv2.addWeighted(undist, 1, newwarp, 0.5, 0)
# Print distance from center on video
if center < 0:
cv2.putText(result, 'Vehicle is {:.2f}m left of center'.format(center_offset_meter), (100,80),fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 2)
else:
cv2.putText(result, 'Vehicle is {:.2f}m right of center'.format(center_offset_meter), (100,80),fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 2)
cv2.putText(result, 'Radius of Curvature {}(m)'.format(int(center)), (120,140),fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 2)
return result
# Print radius of curvature on video
print ('done')
In [29]:
img = cv2.imread("test_images/test2.jpg")
#img = cv2.imread("test_images/straight_lines1.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
Left = Line()
Right = Line()
img2= image_processing(img)
plt.figure(figsize=(10,15))
#plt.figure(figsize=(5,10))
plt.figure(figsize=(30,20))
plt.subplot(2,1,1)
plt.imshow(img2)
plt.subplot(2,1,2)
plt.imshow(img)
Out[29]:
In [30]:
### Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
# Set up lines for left and right
Left = Line()
Right = Line()
video_output = 'result.mp4'
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(image_processing)
white_clip.write_videofile(video_output, audio=False)
In [15]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output ))
Out[15]: